Adaptive Endmembers Learning-Based Deep Unmixing Network for Hyperspectral Change Detection-Reference-Cited by-同舟云学术

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Adaptive Endmembers Learning-Based Deep Unmixing Network for Hyperspectral Change Detection

Published:2024-07-07 Issue: Volume: Page:10333-10337
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Xu Shuyi¹,Cai Luyang¹,Sun He¹,Sun Xu¹,Yang Lina²,Gao Lianru¹

Affiliation:

1. Chinese Academy of Sciences,Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute,Beijing,China,100094

2. Chinese Academy of Sciences,National Engineering Research Center for Geomatics, Aerospace Information Research Institute,Beijing,China,100094

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10640672.pdf?arnumber=10640672

Reference11 articles.

1. Hyperspectral anomalous change detection based on joint sparse representation

2. Hierarchical Unsupervised Change Detection in Multitemporal Hyperspectral Images

3. A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges

4. Hyperspectral change detection: an experimental comparative study

5. Deep Learning-Based Change Detection in Remote Sensing Images: A Review

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